Clustering refers to the task of identifying groups or clusters in a data set. In density-based clustering, a cluster is a set of data objects spread in the data space over a contiguous region of high density of objects. Density-based clusters are separated from each other by contiguous regions of low density of objects. Data objects located in low-density regions are typically considered noise or outliers. In this review article we discuss the statistical notion of density-based clusters, classic algorithms for deriving a flat partitioning of density-based clusters, methods for hierarchical density-based clustering, and methods for semi-supervised clustering. We conclude with some open challenges related to density-based clustering. This article is categorized under: Technologies > Data Preprocessing Ensemble Methods > Structure Discovery Algorithmic Development > Hierarchies and Trees.
|Tidsskrift||Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery|
|Status||Udgivet - apr. 2020|